# Copyright (c) 2018 Presto Labs Pte. Ltd.
# Author: jaewon

import datetime
import functools

from coin.base.datetime_util import to_datetime
from coin.exchange.okex_futures.kr_rest.futures_product import (OkexFuturesProduct)
from coin.exchange.bitmex.kr_rest.futures_product import BitmexFuturesProduct
from coin.strategy.mm.simple_sim import result_util
from coin.strategy.mm.simple_sim.strategy.pass_unhedge_3 import (PassUnhedgedSimStrategy)


def get_products(from_ts):
  return [
      OkexFuturesProduct.FromStr('BTC-USD.QUARTER', current_datetime=from_ts),
      BitmexFuturesProduct.FromStr('BTC-USD.PERPETUAL')
  ]


def get_machines():
  return ['feed-3.eu-west-1']


def get_time_ranges():
  ranges = []
  cur_dt = datetime.datetime(2018, 4, 13, 0, 0, 0)
  end_dt = datetime.datetime(2018, 4, 18, 0, 0, 0)
  while cur_dt < end_dt:
    ranges.append((cur_dt, cur_dt + datetime.timedelta(hours=1)))
    cur_dt += datetime.timedelta(hours=24)
  return ranges


def linear_sell_edge(edge, close_edge, max_pos, pos):
  if pos <= 0.:
    return edge
  elif pos >= max_pos:
    return close_edge
  else:
    p = (pos / float(max_pos))
    return (p * close_edge + (1 - p) * edge)


def linear_buy_edge(edge, close_edge, max_pos, pos):
  if pos >= 0.:
    return edge
  elif pos <= -max_pos:
    return close_edge
  else:
    p = (-pos / float(max_pos))
    return (p * close_edge + (1 - p) * edge)


def get_strategy(from_ts, to_ts):
  products = get_products(from_ts)
  ref_product = products[0]
  ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(200.)[1][-1][0]),
                                 (lambda book: book.get_notional_bids_by_qty(200.)[1][-1][0]))

  pass_product = products[1]
  pass_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(10000.)[1][-1][0]),
                                  (lambda book: book.get_notional_bids_by_qty(10000.)[1][-1][0]))

  NS_PER_SECOND = (10**9)
  NS_PER_MINUTE = 60 * NS_PER_SECOND

  strategy_list = []

  for basis_ma_window in [2]:  # [5, 10, 15]:
    for edge_bp in [4]:  # [5, 7, 9, 11]:
      for close_edge_bp in [-1]:  # [5, 6, 7]:
        for agg_edge in [None, (edge_bp + 8.5) / 10000.]:
          stack = 4
          lot_size = 8
          pricing_param = {
              'basis_ma_window': basis_ma_window * NS_PER_MINUTE,
              'tick': 0.5,
              'book_askt_func_1': ref_product_true_book_funcs[0],
              'book_bidt_func_1': ref_product_true_book_funcs[1],
              'book_askt_func_2': pass_product_true_book_funcs[0],
              'book_bidt_func_2': pass_product_true_book_funcs[1]
          }

          executor_param = {
              'lot_size': lot_size,
              'min_pos': -lot_size * stack,
              'max_pos': lot_size * stack,
              'maker_fee': -2.5 / 10000.,
              'taker_fee': 7.5 / 10000.,
              'execution_delay': 1 * NS_PER_SECOND,
              'post_only': True,
              'ignore_book_fill': True,
              'trade_qty_func': (lambda trade: trade.qty / trade.price)
          }

          use_agg = 'without_agg'
          if agg_edge:
            use_agg = 'with_agg'
          name = '%02dm.%02dbp.%02dbp.%02dstack.%s.%s' % (basis_ma_window,
                                                          edge_bp,
                                                          close_edge_bp,
                                                          stack,
                                                          use_agg,
                                                          to_datetime(from_ts).strftime('%Y%m%d'))

          strategy = PassUnhedgedSimStrategy(pass_product,
                                             ref_product,
                                             functools.partial(linear_sell_edge,
                                                               edge_bp / 10000.,
                                                               close_edge_bp / 10000.,
                                                               1.),
                                             functools.partial(linear_buy_edge,
                                                               edge_bp / 10000.,
                                                               close_edge_bp / 10000.,
                                                               1.),
                                             pricing_param,
                                             executor_param,
                                             trade_after=basis_ma_window,
                                             agg_edge=agg_edge,
                                             name=name,
                                             fill_filepath='/dev/stdout')
          strategy_list.append(strategy)

  return strategy_list


def get_strategy_result(strategy):
  return {'name': strategy.name, **strategy.get_summary()}


def aggregate_result(results):
  return result_util.aggregate_sim_result(results)
